Microbial Growth Parameters Research Articles

Partitioning of the variance in the growth parameters of Erwinia carotovora on vegetable products

The objective of this paper was to estimate and partition the variability in the microbial growth model parameters describing the growth of Erwinia carotovora on pasteurised and non-pasteurised vegetable juice from laboratory experiments performed under different temperature-varying conditions. We partitioned the model parameter variance and covariance components into effects due to temperature profile and replicate using a maximum likelihood technique. Temperature profile and replicate were treated as random effects and the food substrate was treated as a fixed effect. The replicate variance component was small indicating a high level of control in this experiment. Our analysis of the combined E. carotovora growth data sets used the Baranyi primary microbial growth model along with the Ratkowsky secondary growth model. The variability in the microbial growth parameters estimated from these microbial growth experiments is essential for predicting the mean and variance through time of the E. carotovora population size in a product supply chain and is the basis for microbiological risk assessment and food product shelf-life estimation. The variance partitioning made here also assists in the management of optimal product distribution networks by identifying elements of the supply chain contributing most to product variability.

Estimation of Microbial Growth Parameters by Means of Artificial Neural Networks

Estimation of Microbial Growth Parameters by Means of Artificial Neural Networks

Estimation of Microbial Growth Parameters by Means of Artificial Neural Networks

An alternative method based on artificial neural networks (ANN) for the estimation of kinetic growth parameters of a microorganism is performed by applying an automatic regression on different sections of the growth curve so as to obtain more precise growth rate and lag-time values. Through the combination of genetic algorithms and pruning methods more simple neural networks are obtained, where the goodness of fitness is a combination of an error function with another function associated with the network's complexity. An interesting application of this method was the estimation of kinetic parameters in microbial growth (growth rate and lag-time) and specifically in our case, the analysis of the effect of NaCl concentration, pH level and storage temperature on the growth curves of Lactobacillus plantarum. In this study it was hoped that the architecture of the obtained model would be very simple, but still keep its adequate capacity of generalization. The comparison performed between the average standard error of predictions (SEP) obtained for the estimation of growth rate and lag-time by the automatic regression (20 and 24%, respectively) and by the Gompertz estimation (22 and 28%) showed the utility of this method. (A)

Modeling the Aerobic Growth and Decline of Staphylococcus aureus as Affected by pH and Potassium Sorbate Concentration

The effects of pH (5.0, 5.2, 5.4, 5.6, and 5,8) and concentration of potassium sorbate (10.0 and 16.6 mM) at two water activity values (0.90 and 0.92) on the aerobic growth and decline of Staphylococcus aureus ATCC 6538P, 196-E, and FDA-C243 were studied using brain-heart infusion broth. The inoculum was approximately 4 to 5 log CFU/ml, and the incubation temperature was 30°C. Samples were periodically enumerated on tryptic soy agar. The Gompertz model was used to obtain microbial growth parameters, specific growth rate was obtained as a derived parameter, and the inhibition index was calculated. A linear model was fitted in cases of bacteriostatic or bactericidal action of the treatment. The ATCC 6538P strain showed the highest resistance in the range of tested conditions. Microbial behavior was modeled considering the main controlling factors, and a response surface methodology was used to determine the effects of undissociated acid concentration and pH. These results can be used to establish treatment conditions for microorganism growth or inhibition.

Relation between the generation time and the lag time of bacterial growth kinetics

In predictive microbiology, the relation between the lag time ( Lag) and the generation time ( T g) is commonly assumed to be proportional, as long as the pre-incubation environmental conditions remain constant. This relation was statistically examined in nine published datasets. For every dataset, it was roughly proportional. However, a more advanced study showed that the ratio Lag/ Tg was not totally independent of the environmental conditions. In particular, a significant negative effect of the pH on this ratio was observed in five of the nine datasets. For modeling the environmental dependence of microbial growth parameters, some authors independently deal with Lag and Tg. Other authors only model the environmental dependence of Tg, assuming Lag/ Tg to be constant. These two modeling methods were statistically compared for the nine datasets under study. Results differed from one dataset to another. For some, the model developed with a constant ratio Lag/ Tg sufficed to describe the data, whereas for the others, an independent modeling of Lag and Tg was more satisfactory.

Degradation of pectic compounds during pasteurised vegetable juice spoilage by Chryseomonas luteola: a predictive microbiology approach

Predictive modelling consists in describing effects of environmental factors on microbial growth parameters. With food spoilage bacteria, this approach must be extended to both growth and food damage characterisation. In order to study the incidence of storage temperature on vegetable damage, using predictive microbiology tools, kinetics of pectic compound degradation were studied. Chryseomonas luteola has been chosen because of its ability to grow on post-harvested vegetables. Experiments were performed at refrigerated temperatures (0–10°C) with low initial bacterial charges (10 1–10 3.5 cfu/ml). Microbial specific growth rate ( μ), stability phase before pectic degradation ( S p) and alteration percentage ( A p) were chosen as reference parameters. Then, sub-optimal temperature effects on these three parameters were estimated using modified Ratkowsky model. Results obtained in synthetic medium were compared with data observed in endive juice to appreciate the alteration of vegetable during post-harvest storage.

Analysis of microbial I growth data using a spreadsheet

This paper describes the use of computer spreadsheets for the simple mathematical determination of microbial growth parameters. By carrying out laboratory growth experiments using non-pathogenic microorganisms, the student can enter results into a spreadsheet and, using the functions of the spreadsheet, calculate growth parameters such as specific growth rate constant, doubling time, and yield coefficients. The spreadsheet can readily be used for different sets of data from different microorganisms and/or different growth conditions.

Biocenosis diversity and denitrification efficiency

During denitrification carried out in the presence of methanol, glycerol, acetic and lactic acids as a C-source, the microbial growth parameters such as: maximum attainable biomass production ( P x max ), “optimal” biomass residence time ( Θ x ), biomass yield coefficients ( Y N and Y C) and specific biomass loss rate constants ( b N and b C) were estimated. Also, the following process parameters: maximum attainable biomass N and C removal rates ( ΔB x, Nmax and ΔB x, Cmax ) and corresponding rate constants ( K Bx, N and K Bx, C ) were calculated. The values of these parameters were interpreted in relation to the chemical structure of the C-sources for denitrification and degree of their assimilability by the bacteria and fungi which dominated in biocenoses.

Evaluation of a feed intake model for the grazing beef steer.

Responses of a feed intake model for grazing beef cattle to changes in model parameters, forage composition, and supplementation programs with energy and protein were evaluated. Without supplements, the model systematically underpredicted intake of low-quality (low digestibility) forages and subsequent overprediction was observed for high-quality diets. In general, for a reference diet of Italian ryegrass, the model was relatively insensitive to microbial growth parameters, highly sensitive to the microbial carbohydrate composition constant, and moderately sensitive to the microbial N composition constant. Intake prediction was sensitive to changes in the microbial use rate constant for fiber but insensitive to those for protein and starch. Model predictions were highly sensitive to the amount of nondegradable fiber in each of the forages tested. Supplementation effects on forage intake were quantified by supplementing all forage diets with chemical components equivalent to that provided by 1 kg of corn grain or 1 kg of cottonseed meal. Supplementation of the forage diet with the concentrate source resulted in substitution ratios of forage to supplement intake consistent with in vivo results. As forage quality increased, substitution of concentrate for the forage increased. However, the model failed to predict the increased forage intake typically observed with protein supplementation, suggesting that it is insufficient for intake prediction in protein-limiting situations. Nevertheless, the model correctly predicted effects of energy supplementation and forage composition on forage intake, suggesting that different controls must regulate intake responses to supplemental protein.